Manufacturing high quality products safely, quickly, and economically

Thrust Leader: Venkat Venkatasubramanian (Purdue)

A. Thrust Objective and Goals

·     Implement advanced science-based sensor technologies for effective process monitoring and state estimation in the manufacture of engineered composite products

·     Optimize the performance of manufacturing processes by developing and implementing advanced model-based informatics, supervisory control strategies, design and scale-up, and real time optimization methodologies

B. Technical scientific barriers/grand challenges for Thrust D

The overall barriers in manufacturing science deal with the (i) identification of critical material and/or process parameters that need be monitored in real-time, (ii) development of appropriate predictive models, (iii) an informatics framework that can keep track of all the data-information-knowledge in the system, and (iv) an integrated system of hardware and software that can interface with the process and execute all this efficiently for design, control, optimization. The barriers are:

• Identification of critical material and/or process parameters that should be monitored for control
• Detection of property and/or behavior of interest with sufficient resolution to discern changes that cause an undesired change in the behavior of the system.
• Deconvolution of the physical part of the signatures to elucidate significant changes occurring as composite systems of formulation components combine or are altered by processing.
• The development of reduced-order models for optimal control strategies
• Managing enormous amount of disparate data, information, and knowledge
• Integration of data, information, and knowledge for seamless model-based optimal decision making
• System Integration: Interfaces to sensors, models, software tools, solution methods and human decision makers
• Specific Methodology Development: Fault detection and diagnosis, Real time optimization (RTO), and Supervisory control system
• Task Integration: Develop the two-way links to regulatory control, and the internal links among monitoring and fault diagnosis, intelligent fault administration, and real-time optimization.
• Lack of understanding of process behavior and its response to operating conditions and design parameter changes.

C. Strategic Plan and Thrust Connectivity for Thrust D

      The projects in this thrust try to balance the need to advance fundamental manufacturing science issues such as the development of novel sensing techniques, new ontologies, automated reasoning algorithms, and first-principles models of unit operations with the need to advance the technological applications of these in the various test beds. The model development has focused on requisite unit operations such as feeding, mixing, blending, roller compaction, milling, tabletting etc. The tools developed in this thrust cut across the different scales of product development and manufacturing, incorporating the structure-composition-properties models developed in Thrusts A-D, across the three test beds.

The projects address the following logical needs of this systems engineering thrust:

            (i) one needs sensing techniques and methodologies to monitor different aspects of the manufacturing system to determine what is going on (project D-1); (ii) these sensors need to be integrated with hardware platforms for data input/output and control (project D-2); (iii) the myriad data, information, and knowledge that is generated need an integrated framework for its access, sharing, testing and validation, updating, modification, and use (project D-3); (iv) this data, information and knowledge has to be used for decision-making in real-time process control, optimization, and exceptional events management during manufacturing (project D-4); and (v) need methodologies for scale-up and design (project D-5). The project integration is shown in Figure 2P-17.

D. Key Scientific Deliverables for Thrust D

 In addition to these scientific deliverables, there are Thrust-level integrating deliverables that bring together scientific accomplishments from the projects. Individual projects’ scientific focus is designed to help establish the knowledge base of the thrust, hence, this thrust makes strong contributions to the bottom and middle planes of the 3-plane chart as shown in Figure 2P-18. However, the project deliverables also serve to support specific test bed (shown in the top plane in Figure 2P-18) needs in addition to the thrust level goals.

T-D-1.             Lubrication strategies for continuous blending

T-D-2.             Monitoring properties of strip films

T-D-3.             Monitoring continuous blending

T-D-4.             Identification of critical variables in TB3

T-D-5.             Adapt sensors for TB3

T-D-6.             Monitoring critical attributes of product quality in TB1

T-D-7.             Monitoring crucial variables in TB3

T-D-8.             Monitoring critical attributes of product quality in TB2

T-D-9.             Monitoring critical attributes of product quality in TB3

T-D-10.           Hardware integration of TB1

T-D-11.           Link roller compactor with blender output

T-D-12.           Hardware integration of TB2

T-D-13.           Feedback control for roller compactor and milling

T-D-14.           Mechanical feedback requirements for continuous blender

T-D-15.           Hardware integration of TB3

T-D-16.           Feedback control for continuous blender

T-D-18.           Mechanical feedback requirements for TB2

T-D-19.           Mechanical feedback requirements for TB3

T-D-20.           Feedback control for TB2

T-D-21.           Feedback control for TB3

T-D-22.           Informatics integration for TB1

T-D-23.           Informatics integration for TB2

T-D-24.           Informatics integration for TB3

T-D-25.           Enhanced informatics infrastructure

T-D-26.           Real-time process management for TB1

T-D-27.           Real-time process management for TB2

T-D-28.           Real-time process management for TB3

T-D-29.           Enhanced real-time process management.

T-D-30.           Integrated system design for TB1 & TB2

T-D-31.           Integrated system design for TB3

T-D-32.           Enhanced integrated system design

Develop model-based methodologies, and their implementation in hardware/software, for control, exceptional events management, and real-time optimization of continuous pharmaceutical manufacturing operations.